Next Article in Journal
Citizen Participation Practices in the Governance of Local Food Systems: A Literature Review
Previous Article in Journal
Impact of Spatial Characteristics on Gendered Retail Consumption in Seoul: A Gender-Sensitive Urban Planning Perspective
Previous Article in Special Issue
Thinking Out Loud? Internal vs. External Communication of Sustainability in Companies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 14
10.3390/su16145989
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Assessing the Nexus between Supplier and Customer Integration and Environmental Cost Performance: Insights into the Role of Digital Transformation

by
Jianwei Li
1,
Deyu Zhong
2,*,
Haoyu Ru
3 and
Lixia Jia
2
1
Marxism School, Xi’an Shiyou University, Xi’an 710065, China
2
Graduate School of Business Administration, Pukyong National University, Busan 48513, Republic of Korea
3
Graduate School of Economics, Pukyong National University, Busan 48513, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5989; https://doi.org/10.3390/su16145989 (registering DOI)
Submission received: 13 June 2024 / Revised: 30 June 2024 / Accepted: 9 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue Sustainable Business Management: Operations Competitiveness, Digital Competence and Supply Chain Network)
Browse Figures
Versions Notes

Abstract

:
Since the beginning of the 21st century, environmental protection and digital supply chains have increasingly garnered attention. As firms transition to green practices, how to achieve competitive advantage by reducing environmental costs has become one of the key concerns for many business managers. This study aims to examine how supplier and customer integration affect a firm’s environmental cost performance while considering the moderating effect of digital transformation. Following a survey conducted by a professional research agency on 800 Chinese manufacturing firms specializing in green products, the research model was tested using structural equation modeling and hierarchical regression analysis. The results indicate a significant positive relationship between both supplier integration and customer integration with a firm’s environmental cost performance. Additionally, positive digital transformation reinforces the relationship between supplier integration and customer integration. However, there are limitations due to the study’s background, scope, and sample size. This study underscores the impact of supplier integration and customer integration on a firm’s environmental cost performance, as well as the crucial moderating role of digital transformation. It contributes to the growing literature on sustainable supply chain management and provides valuable insights for relevant practitioners.

1. Introduction

As awareness of green concepts and environmental protection continues to rise, businesses are increasingly focusing on sustainable development and implementing green practices to meet governmental requirements for environmental protection and compliance with laws and regulations. Green practices involve measures taken by businesses in their operations to reduce environmental impacts, minimize resource consumption, and decrease environmental costs, thereby helping manufacturing firms enhance environmental performance across a range of supply chain activities. From the standpoint of manufacturing firms, the key to green practices lies in reducing the overall environmental impact of their activities [1]. However, with the homogenization of green practices in the market environment, the operational levels of green practices among many firms are gradually maturing (e.g., green product innovation, environmental performance assessment, green supply chain management, and circular economy initiatives). Green practices have become a key aspect of corporate sustainable development; thus, advocating green practices as a means of gaining a key competitive advantage in today’s fiercely competitive market environment may be somewhat insufficient [2]. Therefore, this study emphasizes that, compared to competing firms, businesses can strengthen existing competitive advantages by controlling environmental costs and improving environmental cost performance. With the development of Industry 4.0, many firms are increasingly focusing on digital transformation and leveraging digital technologies, such as blockchain and the Internet of Things (IoT), for supply chain management [3]. Through digital transformation, firms can utilize information technology to gain more extensive and transparent supply chain information [4]. This not only effectively enhances the level of information sharing between manufacturing firms and key supply chain partners but also improves resource allocation efficiency and environmental performance [5]. Therefore, this study emphasizes the role of digital transformation in strengthening the relationship between supplier integration, customer integration, and environmental cost performance.
Additionally, according to the extended resource-based view, suppliers and customers are key members of the supply chain, and manufacturing firms need to acquire strategic resources from them to enhance environmental performance and achieve competitive advantage. Although there is a growing body of literature on supplier and customer integration enhancing environmental performance or green performance [6,7], research emphasizing environmental costs based on environmental performance remains insufficient. Moreover, there is also a lack of research on how digital transformation affects supplier and customer integration and firm environmental costs. Therefore, this study aims to fill this gap by examining the impact of supplier and customer integration on firm environmental cost performance through analyzing the interactions among these factors. This study will contribute to the existing literature on supplier integration, customer integration, digital transformation, and environmental performance, providing valuable insights for businesses seeking to enhance environmental cost performance as a competitive advantage.
Furthermore, to extend the existing literature, we propose four research questions and two research objectives in this study. Firstly, what is the relationship between supplier integration and firm environmental cost performance? Secondly, what is the relationship between customer integration and firm environmental cost performance? Thirdly, does digital transformation moderate the relationship between supplier integration and firm environmental cost performance? Fourthly, does digital transformation moderate the relationship between customer integration and firm environmental cost performance? To investigate and address the above questions, this study first examines the impact of supplier and customer integration on firm environmental cost performance, including exploring their relationships and determining the extent to which integrating them contributes to improving environmental costs, serving as the first research objective. The second research objective is to examine and evaluate the capability of digital transformation to moderate the relationship between supplier integration and firm environmental cost performance, whether it has a moderating effect, and to determine whether the implemented digital transformation strategies enhance or diminish the impact of supplier and customer integration on company environmental cost performance. By addressing these questions, this study aims to deepen our understanding of the impact of supplier and customer integration on company environmental cost performance and the moderating effects of digital transformation. The research findings will provide valuable insights for organizations seeking to enhance their environmental performance and environmental cost performance.
This study is comprised of seven sections. The first section is the introduction, which presents the research background, objectives, and the necessity of this study. The second section is the literature review and hypothesis development, where a theoretical discussion of the four variables of the structural equation model is conducted, along with an examination of their complex interrelationships. The third section covers the research methodology, detailing the data collection process, reliability and validity analyses, model fit assessments, and other preliminary verification tasks before regression analysis. The fourth section presents the regression results, confirming and supporting our hypotheses. The fifth section is the discussion, exploring the theoretical and practical implications of the research findings within the theoretical framework. The sixth section addresses the limitations of the study and suggestions for future research. Finally, the seventh section lists the references.

2. Literature Review and Hypothesis Development

2.1. Supply Chain Integration

Tarifa Fernández [8] pointed out that cognitive interdependence drives the motivation for integration. In today’s business environment, supply chain integration is becoming increasingly critical, aiming to enhance organizational resilience, flexibility, and sustainability [9,10]. Particularly in uncertain and turbulent contexts, integrated supply chains facilitate effective rapid adaptation, risk management, and sustained operational efficiency [11]. Past research has extensively explored various aspects of supply chain integration, including supplier integration [12], customer integration [13], information integration [14], logistics integration [15], and procurement integration [16]. Huo [17] further classified supply chain integration into internal integration (II), supplier integration (SI), and customer integration (CI), categorizing SI and CI as external integration (EI). External integration involves transforming organizational strategies into operational processes through collaboration with key suppliers and customers [18]. This study situates external integration within the framework of the extended resource-based view (ERBV). ERBV posits that sustained competitive advantage for firms requires the acquisition, development, and utilization of resources that meet the VRIN criteria (i.e., valuable, rare, inimitable, non-substitutable) through inter-organizational cooperation [19]. In this context, supplier integration and customer integration serve as strategic approaches to acquire unique resources from key suppliers, such as specialized skills, cutting-edge technologies, or exclusive raw materials [20], and to access genuine market demands, unique intelligence, and close collaborative relationships from key customers [21], thus meeting the VRIN criteria. This not only enhances the firm’s responsiveness but also transforms relationships with suppliers and customers into valuable and unique resources [18]. This is particularly important in fostering loyalty and generating positive reputations [22]. Thus, integrating suppliers and customers extends the firm’s resource base beyond internal capabilities, contributing to sustained competitive advantage and enhancing sustainability [23].

2.1.1. Supplier Integration

Supplier integration falls under external integration within supply chain integration [24]. It entails collaboration between manufacturers and their key suppliers to consolidate information, resources, strategies, and practices across organizations, establishing a cohesive, consistent, and flexible integrated state [25]. This integration strengthens the cooperative relationship between manufacturing firms and suppliers, thereby maximizing the value of the supply chain [26]. Supplier integration remains a prominent research topic today, with previous literature examining various positive effects it brings, including operational performance [27], financial performance [28], innovation performance [11], and environmental performance [29]. Moreover, an increasing body of literature demonstrates that higher levels of supplier integration yield greater potential effects [30]. Supplier integration encompasses order processes, information sharing, technical support, inventory levels, production planning, and resource allocation [31], which we categorize into three aspects: information sharing, strategic planning, and operational alignment. This study will investigate the impacts of these three dimensions within the framework of extending the resource-based view [32].

2.1.2. Customer Integration

Customer integration and supplier integration both belong to external integration, emphasizing the promotion of coordination across various processes by fostering close relationships between firms and customers, thereby creating a unique relational resource to cultivate competitive advantages [24]. Close interaction between manufacturing firms and customers can enhance the accuracy of information obtained, which not only reduces the bullwhip effect but also improves production planning, reduces inventory, and facilitates product design or innovation while reducing supply chain costs [33,34]. In past research, scholars have extensively studied the positive effects of integrating customers. For example, enhancing customer loyalty [35], strengthening product innovation capabilities [24], optimizing supply chain risk management [36], and sustainable development [31]. These studies have demonstrated the necessity and feasibility of customer integration. Customer integration includes demand management [37], relationship management (CRM) [38], information sharing [39], and cooperative innovation [40]. This study, within the expanded framework of the resource-based view, examines the multifaceted impacts brought about by relationship management and information-sharing dimensions.

2.2. The Effects of Supplier Integration and Customer Integration on Environmental Cost Performance

This study examines three dimensions of supplier integration within the framework of extending the resource-based view: information sharing, strategic planning, and operational integration. Firstly, information sharing facilitates better decision-making among partners, optimizing production schedules and capacity allocation to reduce transaction costs and the bullwhip effect [12,41]. Furthermore, it enables the reduction of energy and material consumption through the sharing of environmental information (such as emission reduction technologies) [12]. Secondly, strategic planning fosters collaborative anticipation and resolution of environmental issues between manufacturing firms and suppliers, thereby reducing the likelihood of environmental accidents during production and minimizing waste disposal costs [42]. Thirdly, operational alignment enhances the efficiency and flexibility of processes such as material movement, procurement, and production [43]. By integrating with the environmental management systems of their key suppliers, manufacturing firms mitigate environmental costs in operations (e.g., reducing fuel consumption during transportation), thereby creating maximum value at low cost and high efficiency [44]. In summary, supplier integration by firms can reduce environmental-related costs and improve environmental cost performance [45]. This study regards this effect as a form of cost-competitive advantage, effectively enhancing sustainability in the ever-changing operational environment. Therefore, this study proposes hypothesis H1.
H1: 
There exists a positive relationship between the level of supplier integration and a firm’s environmental cost performance.
The dimensions of relationship management and information sharing in customer integration have numerous potential impacts on environmental costs. Firstly, given the various regulatory pressures from environmental institutions in the societal and policy context where manufacturing firms operate, businesses face the dual challenge of meeting environmental requirements from regulatory bodies while satisfying market demands [46]. Consequently, firms need to allocate significant additional resources and costs to fulfill these conditions [47]. However, manufacturing firms can address this issue through customer integration. By establishing close relationships with customers, firms can obtain the latest market demand information from them. Guided by this information, firms can better adjust production plans and economize on additional usage and wastage of resources during operations [48]. Secondly, by integrating their environmental management systems with key customers, firms can better understand customer demand for environmentally friendly products and services. This enables them to consider these factors during the production process, thereby meeting both market demand and environmental regulatory requirements [49]. Additionally, collaborating with customers to forecast and address environmental issues can reduce the overall environmental impact of their production and operational activities [50]. Such customer integration aids firms in more effectively utilizing relational resources to reduce environmental costs and enhance sustainable development. Therefore, this study proposes hypothesis H2.
H2: 
There exists a positive relationship between the level of customer integration and a firm’s environmental cost performance.

2.3. Environmental Cost Performance

Environmental cost refers to the array of expenses and capital outlays that firms incur in their environmental practices, constituting a pivotal aspect of green supply chain management [12]. This encompasses costs and capital investments associated with resource consumption, pollution control, and environmental protection throughout the stages of innovation [51], transportation [52], green performance monitoring [53], and production of green products or services [54]. Gast et al. [55] argue that environmental cost stands as a fundamental driver for sustainable business development. The potential impacts of environmental cost performance span economic, social, and institutional domains. Economically, it includes reducing energy consumption or pollution treatment costs through the utilization of green materials [56]. Socially, proactive environmental measures contribute to enhancing green reputation and brand image [57]. Institutionally, reducing the negative environmental impact of operations can enhance a firm’s ability to comply with environmental regulations [23]. Driven by these positive influences, manufacturing firms are increasingly prioritizing environmental costs [58].
Moreover, environmental sustainability is progressively being integrated into supply chain management and operations within the literature. This integration encompasses green supply chain management aimed at reducing environmental impacts and optimizing resource utilization [59], environmental management systems (EMS) emphasizing environmental policies and management reviews [60], and life cycle assessment (LCA) focusing on data collection and analysis of environmental impacts [61]. Although these three technologies have distinct focal points, they collectively underscore environmental sustainability in business operations. This study regards environmental cost performance as a crucial means for manufacturing firms to maintain and enhance market competitiveness, aiming to attain sustainability-driven competitive advantages and lay a solid foundation for long-term competitive edges by improving environmental cost performance.

2.4. Digital Transformation

Digital transformation refers to the process wherein firms utilize digital technologies to optimize their business operational processes, aiming to attain sustainable operations and competitive advantages [62]. In today’s fiercely competitive market landscape, many managers are increasingly focusing on digitalizing supply chains, as they perceive digital transformation as having developmental potential within the Industry 4.0 environment [63]. The existing literature has extensively examined the specific positive effects brought about by digital transformation (DT), which can be categorized into two main aspects: operations and relationships. In terms of operations management, DT has been found to enhance productivity, flexibility, transparency, and innovation efficiency [64]. Moreover, in relationship management, DT ensures the timeliness and reliability of information, as well as the stability of close relationships with partners [65]. Through supply chain digitalization, firms are able to promptly grasp the dynamic demands of all partners in the supply chain, thereby saving unnecessary resource wastage and additional cost expenditures [66].
This study examines the digital transformation of supply chains within the framework of dynamic capability theory. Dynamic capability theory is a mature theoretical framework that emphasizes how firms acquire, integrate, and utilize resources to attain sustainable competitive advantages [67]. Firms implementing digital transformation strategies within the supply chain aim to enhance operational efficiency and reduce environmental costs through digital capabilities in pursuit of sustainable development [68].

2.5. The Moderation Effects of Digital Transformation

Effective information management is one of the key mechanisms in contemporary supply chain management, as traditional supply chain management suffers from issues of inflexibility and lack of timeliness, particularly in external integration by firms [69]. For instance, cooperation across organizations may encounter coordination or timeliness challenges, leading to additional costs (e.g., transaction costs, processing costs) [70]. However, with digital transformation, firms can effectively address these issues by leveraging digital technologies such as information technology platforms [64]. This study examines the moderating role of digital transformation on the relationship between supplier-customer integration and environmental cost performance within the framework of dynamic capability theory. Dynamic capability theory emphasizes the acquisition, integration, and utilization of resources to adapt to environmental changes [67].
Firms enhance their relationships with suppliers and customers through digital technologies, which can reduce environmental costs incurred in production operations, inventory management, and supply–demand management [18]. Firstly, in the production and operation processes, the efficient utilization of resources such as raw materials, information, and technology obtained through close relationships with key partners can effectively reduce energy and material consumption [71]. Furthermore, integrating suppliers’ and customers’ environmental management systems through digital technologies can meet the requirements of relevant environmental regulatory bodies, thereby reducing environmental costs incurred due to non-compliance with environmental regulations [72]. Secondly, through digital technologies, such as RFID or electronic ordering, more precise inventory tracking management can be achieved to reduce resource waste and environmental costs resulting from excessive inventory. Additionally, data analytics enable firms to more accurately predict customer demand and supplier delivery times, thereby reducing excess inventory and waste and lowering environmental costs [73]. Thirdly, advanced information technology systems can enhance the visibility and transparency of information flow in the supply chain, reducing information asymmetry and effectively managing supply demand. Firms can more effectively select and manage partners that meet their environmental standards and monitor and adjust various environmental aspects of the supply chain in real-time through digital tools, thereby not only reducing environmental costs but also improving environmental performance [74]. In summary, digital transformation not only facilitates effective management of inter-organizational relationships and enhances operational efficiency but also effectively reduces environmental costs, thus practicing sustainability through high levels of environmental cost performance. Therefore, this study proposes hypotheses 3a and 3b.
H3a: 
The positive relationship between supplier integration and environmental cost performance is strengthened when a firm undergoes a high-level digital transformation.
H3b: 
The positive relationship between customer integration and environmental cost performance is strengthened when a firm undergoes a high-level digital transformation.
The specific structural equation model of this study is as follows (see Figure 1).

3. Research Method

3.1. Overview of Research Methods

This study employs structural equation modeling (SEM) to test the research model because SEM allows for the evaluation of complex relationships between observed and latent variables. It is particularly suitable for examining both direct and indirect effects among variables. The research model is designed to analyze the interactions between supplier integration, customer integration, digital transformation, and environmental cost performance. Specifically, it investigates the direct impact of supplier integration and customer integration on environmental cost performance, as well as the moderating role of digital transformation on these relationships.
To examine the relationships among these variables, the study first defines the operational measures for each variable based on prior research (for details, refer to Section 3.3) and creates a survey questionnaire to collect data accordingly. The data collection procedure is elaborated in Section 3.2. After data collection, statistical methods were applied to mitigate common method bias (for details, refer to Section 3.5). Subsequently, we performed confirmatory factor analysis (CFA) using AMOS 23.0 to assess the unidimensionality of the measurement items and the model fit (for details, refer to Section 3.4). Finally, we used SPSS 27.0 to conduct hierarchical regression analysis to test the hypotheses (for details, refer to Section 4). In summary, this study thoroughly examines the proposed research model using robust methodological approaches to validate the relationships between supplier and customer integration, digital transformation, and environmental cost performance.

3.2. Data Collection Procedures

This study delves into the intricate interplay between supplier integration, customer integration, digital transformation, and environmental cost performance within China’s manufacturing sector. Encompassing diverse industries such as textiles, furniture, chemicals, pharmaceuticals, automotive manufacturing, and electrical machinery equipment, it aims to illuminate crucial connections. To kickstart the investigation, researchers crafted a meticulously structured questionnaire in English. To guarantee precision and content validity, bilingual experts undertook its thorough translation. Prior to widespread dissemination, a pilot test was executed, and participant feedback was diligently integrated into questionnaire modifications. Continuous communication via email and WeChat served to elucidate any inquiries and ensure respondents’ comprehensive understanding.
The study employed a systematic sampling method to pinpoint target firms, spanning various manufacturing sectors in China, classified according to Chinese industry codes. These sectors include textiles (P89), furniture (Y80), pharmaceuticals and chemicals (C10), automotive (T40), and electrical machinery (N20). The rationale behind opting for the systematic sampling method lies in its capacity to offer a structured and efficient representation of Chinese manufacturing firms. Within specific industry codes, the sample frame comprised 1600 firms, with systematic sampling ensuring a methodical and unbiased selection process. Starting from December 2023, a three-month data collection endeavor concentrated on a sample list consisting of 800 Chinese manufacturing firms exclusively engaged in producing green products. Survey distribution adhered to the total design method (TDM) as proposed by Dillman [75], with survey links dispatched at two-week intervals along with three reminders. Among the 800 firms contacted, 418 consented to participate and furnished their contact information. However, only 324 respondents completed the survey, resulting in a response rate of 40.5%. Out of the collected 382 responses, this study meticulously excluded 13 responses deemed invalid due to reporting discrepancies (such as insincere reporting or missing variables), ensuring that 311 responses formed the basis for the final analysis. Table 1 furnishes detailed demographic characteristics, providing insights into representative firm types, age, size, annual sales revenue, and pertinent factors.

3.3. Measurement Items

Table 2 of this study was meticulously crafted through a thorough review of past research and established theoretical frameworks. It operationalizes key constructs by employing survey items, ensuring their validity and relevance within the research context. Supplier integration and customer integration each encompass four items adapted from Xie et al. [76], gauging firms’ shared information and strategic collaboration with both suppliers and customers to address environmental concerns. Environmental cost performance is comprised of four measurement items sourced from Wong, Wong and Boon-itt [12], assessing the degree to which firms mitigate pollution, waste, emissions, and the adverse environmental impacts of their products. Digital transformation is evaluated through five measurement items drawn from [64], examining the extent to which firms implement digital transformation strategies within their supply chains. All these items were evaluated using a seven-point Likert scale.
Additionally, the study incorporates five control variables: firm size (transformed into the logarithmic form of the number of employees), firm age (on a logarithmic scale), average annual sales revenue (also on a logarithmic scale), investment in eco-friendly products (again on a logarithmic scale), and industry classification (transformed into dummy variables). Detailed measurement items for each variable are provided in Table 2.

3.4. Construct Validity

In preparation for hypothesis testing, a comprehensive Confirmatory Factor Analysis (CFA) was undertaken using AMOS 23.0 to evaluate the unidimensionality of measurement items. CFA was chosen over other techniques, such as coefficient α and Exploratory Factor Analysis [77], due to its more stringent interpretation of unidimensionality.
Results presented in Table 2 demonstrate favorable fit indices for all items: χ2/df = 1.492, RMSEA = 0.031, CFI = 0.979, and IFI = 0.978. These robust fit indices substantiate the adoption of the proposed measurement model and confirm the unidimensionality of measurement items. Furthermore, construct reliability was assessed using Cronbach’s α coefficient, yielding α values ranging from 0.847 to 0.911, surpassing the recommended threshold of 0.700 [78]. To further evaluate validity, both convergent and discriminant validity were examined. Initially, convergent validity was established by the range of standardized factor loadings (SFL) from 0.768 to 0.842, surpassing the threshold of 0.5; each construct’s composite reliability (CR) ranged from 0.847 to 0.911, exceeding 0.700; and each construct’s average variance extracted (AVE) ranged from 0.613 to 0.694, surpassing the benchmark of 0.500 [79].
Additionally, following the principles proposed by Fornell and Larcker [79], correlation analysis was conducted to confirm discriminant validity by comparing the square root of the AVE of each construct with the square correlations between specific constructs and other constructs. The lowest AVE value (0.613) exceeded the highest square correlation value (0.376), thus affirming discriminant validity among the tested constructs. See Table 3.

3.5. Non-Response Bias and Common Method Bias

The study employed procedural and statistical methods to mitigate common method biases. Procedurally, we ensured questionnaire conciseness and clarity with distinct sections for each construct’s measurement items. Respondents, primarily mid-level managers and above, with over 85% possessing more than 5 years of work experience, were selected while ensuring anonymity [80]. These measures aimed to facilitate careful and honest responses, thereby ensuring reliable information sources. Furthermore, to address potential non-response bias, we compared characteristics between early and late respondents, including firm size, industry type, firm age, and annual sales revenue [81]. The analysis revealed no significant differences, confirming the absence of non-response bias in this study.
Additionally, to mitigate common method biases statistically, we employed two methods. Firstly, factor analysis identified four factors, with the largest factor explaining 37.482% of the total variance, indicating that common method bias was not a major concern. Subsequently, confirmatory factor analysis showed similarity in fit indices between the original model (χ2/df = 1.492, RMSEA = 0.031, CFI = 0.979, and IFI = 0.978) and the extended model (χ2/df = 1.481, RMSEA = 0.025, CFI = 0.983, and IFI = 0.984), suggesting that common method bias was not significant in our study [80].

4. Hypothesis Results

We conducted hierarchical regression analysis using SPSS 27.0, employing systematic variable blocking and stepwise testing methods to rigorously assess our hypotheses. This statistical technique was chosen to evaluate the extent to which changes in continuous variables could be explained by highly correlated predictor variables [82]. The hierarchical approach allows for a thorough exploration of complex relationships among multiple variables, considering potential influences from different subgroups and controlling for confounding variables. The hierarchical regression model can be represented as:
Y = β0 + β1 X1 + β2 X2 +… + βk Xk + ϵ
where Y is the dependent variable, X1, X2, …, Xk are the independent variables, β0 is the intercept, β1, β2, …, βk are the coefficients, and ϵ\epsilonϵ is the error term.
Main effects equation:
ECP = β0 + β1SI + β2CI + β3DT + ϵ
Interaction effects equation:
ECP = β0 + β1SI + β2CI + β3DT + β4(SI × DT) + β5(CI × DT) + ϵ
Notes: Supplier integration (SI); customer integration (CI); digital transformation (DT); and environmental cost performance (ECP)
Before proceeding to hypothesis testing, we constructed four models. Model 1 (M1) included only control variables (e.g., firm size, investment, and industry sector) to explore their interactions with environmental cost performance. In M2, two main predictor variables (supplier integration and customer integration) were introduced. Model 3 (M3) expanded upon Model 2 by introducing a moderating variable (i.e., digital transformation). Model 4 (M4) introduced interaction effects, including interactions between supplier integration and digital transformation, and customer integration and digital transformation. The hierarchical regression formulas for the four models are as follows:
Model 1:
ECP = β0 + β1Control Variables + ϵ
Model 2:
ECP = β0 + β1Control Variables + β2SI + β3CI + ϵ
Model 3:
ECP = β0 + β1Control Variables + β2SI + β3CI + β4DT + ϵ
Model 4:
ECP = β0 + β1Control Variables + β2SI + β3CI + β4DT + β5(SI × DT) + β6(CI × DT) + ϵ
The results indicated that in M1, no variables were statistically significant. In M2, the introduction of the independent variable supplier integration showed a significant relationship with firm environmental cost performance (β = 0.407, p < 0.000), supporting Hypothesis 1. Similarly, the independent variable customer integration showed a significant relationship with firm environmental cost performance (β = 0.394, p < 0.000), supporting Hypothesis 2. M3 demonstrated the statistical significance of the moderating variable: digital transformation (β = 0.356, p < 0.000). In M4, interaction effects were evident, with a positive interaction between supplier integration and regulatory protection (β = 0.286, p < 0.000) and statistically significant positive effects of the interaction between supplier integration and digital transformation (β = 0.362, p < 0.000), as well as the interaction between customer integration and digital transformation (β = 0.394, p < 0.000). All research hypotheses meet the confirmation threshold (significance less than 0.05; the direction of regression results aligns with the research hypotheses); therefore, we confirm that all our research hypotheses are supported. For detailed hypothetical results, please refer to Table 4.
Additionally, to illustrate moderation effects, we graphically depicted the relationship between supplier integration and customer integration with environmental cost performance, considering high and low levels of digital transformation. High and low values were defined as deviations of ±1 standard deviation from the mean [83]. Table 5 provides detailed regression results, while Figure 2 intuitively demonstrate the moderation effects, offering academic visualization of the interactions between variables.

5. Discussions

Our research integrates the extended resource-based view (ERBV) and dynamic capabilities theory (DCT) to explore the impact of supplier and customer integration on environmental cost performance. The extended resource-based view emphasizes that strategic resources obtained through inter-firm cooperation across boundaries can reduce unnecessary environmental costs, while Dynamic Capabilities Theory underscores the acquisition, integration, and utilization of resources to lower additional environmental costs.
Our research focuses on recognizing the significant enhancing role of strategic supplier and customer integration on environmental cost performance. Drawing from the extended resource-based view (ERBV), our study investigates the critical role of supplier and customer integration in reducing environmental costs, considering their fundamental role in reducing environmental costs in production, waste management, and transportation processes. Within this conceptual framework, we propose the mechanism of external integration (i.e., supplier and customer) for accessing strategic resources. Our empirical findings provide support for this hypothesis, indicating that manufacturing firms can leverage close relationships with key partners to obtain critical means of reducing environmental costs by integrating suppliers and customers into their environmental management systems. From an academic perspective, our findings align with the ERBV and DCT perspectives, emphasizing the interaction with external entities to gain a competitive advantage. This study innovatively integrates the ERBV and DCT theories, emphasizing the acquisition of strategic resources through external integration and the utilization of dynamic capabilities to reduce environmental costs. This theoretical framework diverges from the traditional single-theory application approach, offering a more comprehensive perspective. Furthermore, compared to conventional methods, this study highlights the critical role of digital transformation in moderating the impact of supplier and customer integration on environmental cost performance. Digital transformation is not merely viewed as a tool but is considered a key moderating factor influencing firms’ environmental performance. Furthermore, our results confirm the empirical evidence of the crucial role of supplier and customer integration in environmental cost performance [12] while also highlighting the pivotal role of digital transformation in reducing environmental costs. Thus, our research both validates and integrates existing theoretical frameworks while emphasizing the critical roles of supplier and customer integration, as well as digital transformation.
Based on our research findings, we emphasize several practical implications. Firstly, supplier and customer integration can effectively reduce the overall environmental impact of their activities, enhance environmental management efficiency and green performance, and lower environmental costs. For example, practices such as co-developing green products, optimizing transportation routes, and improving waste management can effectively reduce environmental impact. Additionally, through integration, the collaborative optimization of production processes and supply chain management can minimize resource waste and energy consumption, thereby reducing unnecessary and additional environmental cost inputs and expenditures. Moreover, digital transformation in manufacturing enterprises can yield benefits such as enhanced operational transparency, reduced information asymmetry, and bounded rationality among managers, as well as diminished opportunistic behavior among partners, all of which contribute to lower environmental costs. For instance, digital transformation can increase operational transparency, enabling the timely identification and resolution of environmental issues. Furthermore, digital technologies can mitigate information asymmetry across the supply chain, improving cooperation efficiency and reducing additional environmental costs arising from information discrepancies. This transparency also curtails opportunistic behavior between manufacturing enterprises and their partners, thereby effectively reducing associated environmental risks and costs. Therefore, through supplier and customer integration coupled with digital transformation, firms can effectively address green environmental issues while promoting environmental sustainability, thus enhancing their competitive position.

6. Limitations and Future Research Directions

This study is subject to notable limitations in geographical scope, methodological approach, and theoretical framework. Firstly, data collection relies entirely on a single country, particularly Chinese corporate data, thus constraining the generalizability of our findings. Consequently, extrapolating our research results to other international regions entails uncertainty. Despite the fact that the Chinese business environment provides a noteworthy research backdrop, it must be acknowledged that distinct backgrounds and cultural differences exist among different countries, which could significantly influence managerial decision-making. Hence, future research endeavors should expand this model to encompass diverse cultural contexts and incorporate national-level variables, including policy and legal frameworks, to account for factors stemming from different backgrounds (such as environmental protection, regulatory pressures, and uncertainty), thereby fostering a more comprehensive understanding and validation of the proposed relationships.
Moreover, methodological limitations are evident in this study, primarily due to the use of subjective rating scales to measure firms’ environmental cost performance. Incorporating objective scales and integrating actual data would greatly enhance the reliability of our findings while alleviating concerns associated with biases related to societal expectations. In subsequent research, an improved feasible approach might involve incorporating objective data points such as environmental impact assessments (EIAs), social cost–benefit analysis, and other relevant indicators to objectively assess firms’ environmental cost performance. Additionally, the relatively small sample size utilized in this study necessitates expansion in future research endeavors to bolster the robustness and generalizability of the proposed model.
Lastly, from a theoretical standpoint, this study predominantly emphasizes the moderating effects of digital transformation on the relationship between integration mechanisms and environmental costs, with limited exploration of moderation mechanisms beyond the scope of digital capabilities. However, it is noteworthy that future research should delve deeper and specify the interactions between mechanisms of digital transformation (such as specific digital technologies) and integration processes, further investigating their complex relationships with environmental costs. Additionally, future research should expand to different countries and regions, considering the influence of various cultural and policy environments on the research outcomes to enhance the generalizability and applicability of the results. More objective data, such as environmental impact assessments (EIAs) and social cost–benefit analyses, should be utilized to improve the reliability and validity of the findings. Furthermore, future research can delve deeper into the study of digital transformation, specifically investigating the impacts of particular digital technologies (such as blockchain, the Internet of Things, and big data analytics) on supplier and customer integration, as well as on environmental cost performance. Research in this direction holds considerable potential in advancing our understanding of these multifaceted dynamics and provides fruitful avenues for future scholarly inquiry.

Author Contributions

Conceptualization, J.L. and D.Z.; methodology, D.Z.; software, D.Z.; validation, D.Z. and J.L.; formal analysis, J.L.; investigation, H.R. and L.J.; resources, H.R. and L.J.; data curation, H.R. and L.J.; writing—original draft preparation, D.Z.; writing—review and editing, J.L.; visualization, H.R.; supervision, D.Z.; project administration, D.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [2022 Shaanxi Provincial Department of Education Science and Technology Project Research on Innovative Approaches to Digital Work in Grassroots Organizations in Shaanxi Province] grant number [Project No. 22JK0147] and The APC was funded by [Jianwei Li]” in this section.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Sanchez-Planelles, J.; Segarra-Oña, M.; Peiro-Signes, A. Identifying different sustainable practices to help companies to contribute to the sustainable development: Holistic sustainability, sustainable business and operations models. Corp. Soc. Responsib. Environ. Manag. 2022, 29, 904–917. [Google Scholar] [CrossRef]
  2. Chan, R.Y.; He, H.; Chan, H.K.; Wang, W.Y. Environmental orientation and corporate performance: The mediation mechanism of green supply chain management and moderating effect of competitive intensity. Ind. Mark. Manag. 2012, 41, 621–630. [Google Scholar] [CrossRef]
  3. Bhat, S.A.; Huang, N.-F.; Sofi, I.B.; Sultan, M. Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture 2021, 12, 40. [Google Scholar] [CrossRef]
  4. Dospinescu, O.; Dospinescu, N. The use of information technology toward the ethics of food safety. Ecoforum J. 2018, 7. [Google Scholar]
  5. Mastos, T.D.; Nizamis, A.; Terzi, S.; Gkortzis, D.; Papadopoulos, A.; Tsagkalidis, N.; Ioannidis, D.; Votis, K.; Tzovaras, D. Introducing an application of an industry 4.0 solution for circular supply chain management. J. Clean. Prod. 2021, 300, 126886. [Google Scholar] [CrossRef]
  6. Wiredu, J.; Yang, Q.; Sampene, A.K.; Gyamfi, B.A.; Asongu, S.A. The effect of green supply chain management practices on corporate environmental performance: Does supply chain competitive advantage matter? Bus. Strategy Environ. 2024, 33, 2578–2599. [Google Scholar] [CrossRef]
  7. Sharma, A.; Borah, S.B.; Haque, T.; Adhikary, A. Engaging customers and suppliers for environmental sustainability: Investigating the drivers and the effects on firm performance. J. Acad. Mark. Sci. 2024, 1–26. [Google Scholar] [CrossRef]
  8. Tarifa Fernández, J. Dependence and resource commitment as antecedents of supply chain integration. Bus. Process Manag. J. 2022, 28, 23–47. [Google Scholar] [CrossRef]
  9. Ramos, E.; Patrucco, A.S.; Chavez, M. Dynamic capabilities in the “new normal”: A study of organizational flexibility, integration and agility in the Peruvian coffee supply chain. Supply Chain Manag. Int. J. 2023, 28, 55–73. [Google Scholar] [CrossRef]
  10. Long, Y.; Feng, T.; Fan, Y.; Liu, L. Adopting blockchain technology to enhance green supply chain integration: The moderating role of organizational culture. Bus. Strategy Environ. 2023, 32, 3326–3343. [Google Scholar] [CrossRef]
  11. Qu, K.; Liu, Z. Green innovations, supply chain integration and green information system: A model of moderation. J. Clean. Prod. 2022, 339, 130557. [Google Scholar] [CrossRef]
  12. Wong, C.Y.; Wong, C.W.; Boon-itt, S. Effects of green supply chain integration and green innovation on environmental and cost performance. Int. J. Prod. Res. 2020, 58, 4589–4609. [Google Scholar] [CrossRef]
  13. Fianko, A.O.; Essuman, D.; Boso, N.; Muntaka, A.S. Customer integration and customer value: Contingency roles of innovation capabilities and supply chain network complexity. Supply Chain Manag. Int. J. 2023, 28, 385–404. [Google Scholar] [CrossRef]
  14. Jiang, Y.; Feng, T.; Huang, Y. Antecedent configurations toward supply chain resilience: The joint impact of supply chain integration and big data analytics capability. J. Oper. Manag. 2024, 70, 257–284. [Google Scholar] [CrossRef]
  15. He, P.; He, Y.; Tang, X.; Ma, S.; Xu, H. Channel encroachment and logistics integration strategies in an e-commerce platform service supply chain. Int. J. Prod. Econ. 2022, 244, 108368. [Google Scholar] [CrossRef]
  16. Utama, D.M.; Santoso, I.; Hendrawan, Y.; Dania, W.A.P. Integrated procurement-production inventory model in supply chain: A systematic review. Oper. Res. Perspect. 2022, 9, 100221. [Google Scholar] [CrossRef]
  17. Huo, B. The impact of supply chain integration on company performance: An organizational capability perspective. Supply Chain Manag. Int. J. 2012, 17, 596–610. [Google Scholar] [CrossRef]
  18. Nayal, K.; Raut, R.D.; Yadav, V.S.; Priyadarshinee, P.; Narkhede, B.E. The impact of sustainable development strategy on sustainable supply chain firm performance in the digital transformation era. Bus. Strategy Environ. 2022, 31, 845–859. [Google Scholar] [CrossRef]
  19. Baah, C.; Opoku Agyeman, D.; Acquah, I.S.K.; Agyabeng-Mensah, Y.; Afum, E.; Issau, K.; Ofori, D.; Faibil, D. Effect of information sharing in supply chains: Understanding the roles of supply chain visibility, agility, collaboration on supply chain performance. Benchmarking Int. J. 2022, 29, 434–455. [Google Scholar] [CrossRef]
  20. Sahoo, S.; Kumar, A.; Upadhyay, A. How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition. Bus. Strategy Environ. 2023, 32, 551–569. [Google Scholar] [CrossRef]
  21. Hofacker, C.; Golgeci, I.; Pillai, K.G.; Gligor, D.M. Digital marketing and business-to-business relationships: A close look at the interface and a roadmap for the future. Eur. J. Mark. 2020, 54, 1161–1179. [Google Scholar] [CrossRef]
  22. Lin, W.L. The role of corporate social responsibility and corporate social irresponsibility in shaping corporate reputation: An analysis of competitive action and innovation strategies. Corp. Soc. Responsib. Environ. Manag. 2024, 31, 1451–1468. [Google Scholar] [CrossRef]
  23. Appannan, J.S.; Mohd Said, R.; Ong, T.S.; Senik, R. Promoting sustainable development through strategies, environmental management accounting and environmental performance. Bus. Strategy Environ. 2023, 32, 1914–1930. [Google Scholar] [CrossRef]
  24. Freije, I.; de la Calle, A.; Ugarte, J.V. Role of supply chain integration in the product innovation capability of servitized manufacturing companies. Technovation 2022, 118, 102216. [Google Scholar] [CrossRef]
  25. Govindan, K.; Jha, P. Modelling of barriers in implementing sustainable manufacturer-supplier collaboration and coping strategies. J. Clean. Prod. 2024, 434, 139635. [Google Scholar]
  26. Fontoura, P.; Coelho, A. More cooperative… more competitive? Improving competitiveness by sharing value through the supply chain. Manag. Decis. 2022, 60, 758–783. [Google Scholar] [CrossRef]
  27. Duhaylongsod, J.B.; De Giovanni, P. The impact of innovation strategies on the relationship between supplier integration and operational performance. Int. J. Phys. Distrib. Logist. Manag. 2019, 49, 156–177. [Google Scholar] [CrossRef]
  28. Rodríguez-González, R.M.; Maldonado-Guzman, G.; Madrid-Guijarro, A. The effect of green strategies and eco-innovation on Mexican automotive industry sustainable and financial performance: Sustainable supply chains as a mediating variable. Corp. Soc. Responsib. Environ. Manag. 2022, 29, 779–794. [Google Scholar] [CrossRef]
  29. Shah, N.; Soomro, B.A. Internal green integration and environmental performance: The predictive power of proactive environmental strategy, greening the supplier, and environmental collaboration with the supplier. Bus. Strategy Environ. 2021, 30, 1333–1344. [Google Scholar] [CrossRef]
  30. Abdelilah, B.; El Korchi, A.; Amine Balambo, M. Agility as a combination of lean and supply chain integration: How to achieve a better performance. Int. J. Logist. Res. Appl. 2023, 26, 633–661. [Google Scholar] [CrossRef]
  31. Awwad, A.; Anouze, A.L.M.; Ndubisi, N.O. Green customer and supplier integration for competitive advantage: The mediation effect of sustainable product innovation. Sustainability 2022, 14, 10153. [Google Scholar] [CrossRef]
  32. Pereira, V.; Bamel, U. Extending the resource and knowledge based view: A critical analysis into its theoretical evolution and future research directions. J. Bus. Res. 2021, 132, 557–570. [Google Scholar] [CrossRef]
  33. Jeong, K.; Hong, J.-D. The impact of information sharing on bullwhip effect reduction in a supply chain. J. Intell. Manuf. 2019, 30, 1739–1751. [Google Scholar] [CrossRef]
  34. Xu, D.; Long, Y. The role of supply chain integration in the transformation of food manufacturers: A case study from China. Int. J. Logist. Res. Appl. 2021, 24, 198–212. [Google Scholar] [CrossRef]
  35. Gao, M.; Huang, L. Quality of channel integration and customer loyalty in omnichannel retailing: The mediating role of customer engagement and relationship program receptiveness. J. Retail. Consum. Serv. 2021, 63, 102688. [Google Scholar] [CrossRef]
  36. Munir, M.; Jajja, M.S.S.; Chatha, K.A.; Farooq, S. Supply chain risk management and operational performance: The enabling role of supply chain integration. Int. J. Prod. Econ. 2020, 227, 107667. [Google Scholar] [CrossRef]
  37. Rajani, R.L.; Heggde, G.S.; Kumar, R.; Chauhan, P. Demand management strategies role in sustainability of service industry and impacts performance of company: Using SEM approach. J. Clean. Prod. 2022, 369, 133311. [Google Scholar] [CrossRef]
  38. Migdadi, M.M. Knowledge management, customer relationship management and innovation capabilities. J. Bus. Ind. Mark. 2020, 36, 111–124. [Google Scholar] [CrossRef]
  39. Tang, Y.M.; Chau, K.Y.; Ip, Y.K.; Ji, J. Empirical research on the impact of customer integration and information sharing on supply chain performance in community-based homestays in China. Enterp. Inf. Syst. 2023, 17, 2037161. [Google Scholar] [CrossRef]
  40. Ozdemir, S.; de Arroyabe, J.C.F.; Sena, V.; Gupta, S. Stakeholder diversity and collaborative innovation: Integrating the resource-based view with stakeholder theory. J. Bus. Res. 2023, 164, 113955. [Google Scholar] [CrossRef]
  41. Yang, Y.; Lin, J.; Liu, G.; Zhou, L. The behavioural causes of bullwhip effect in supply chains: A systematic literature review. Int. J. Prod. Econ. 2021, 236, 108120. [Google Scholar] [CrossRef]
  42. Bouguerra, A.; Hughes, M.; Rodgers, P.; Stokes, P.; Tatoglu, E. Confronting the grand challenge of environmental sustainability within supply chains: How can organizational strategic agility drive environmental innovation? J. Prod. Innov. Manag. 2024, 41, 323–346. [Google Scholar] [CrossRef]
  43. Enrique, D.V.; Lerman, L.V.; de Sousa, P.R.; Benitez, G.B.; Santos, F.M.B.C.; Frank, A.G. Being digital and flexible to navigate the storm: How digital transformation enhances supply chain flexibility in turbulent environments. Int. J. Prod. Econ. 2022, 250, 108668. [Google Scholar] [CrossRef]
  44. Asiaei, K.; Bontis, N.; Alizadeh, R.; Yaghoubi, M. Green intellectual capital and environmental management accounting: Natural resource orchestration in favor of environmental performance. Bus. Strategy Environ. 2022, 31, 76–93. [Google Scholar] [CrossRef]
  45. Zhao, Y.; Zhao, C.; Guo, Y.; Sheng, H.; Feng, T. Green supplier integration and environmental innovation in Chinese firms: The joint effect of governance mechanism and trust. Corp. Soc. Responsib. Environ. Manag. 2021, 28, 169–183. [Google Scholar] [CrossRef]
  46. Aragòn-Correa, J.A.; Marcus, A.A.; Vogel, D. The effects of mandatory and voluntary regulatory pressures on firms’ environmental strategies: A review and recommendations for future research. Acad. Manag. Ann. 2020, 14, 339–365. [Google Scholar] [CrossRef]
  47. Lv, C.; Shao, C.; Lee, C.-C. Green technology innovation and financial development: Do environmental regulation and innovation output matter? Energy Econ. 2021, 98, 105237. [Google Scholar] [CrossRef]
  48. Bag, S.; Rahman, M.S.; Sharma, J.; Chiarini, A.; Srivastava, S.K.; Gupta, S. Building digital technology and innovative lean management capabilities for enhancing operational performance: An empirical study. Prod. Plan. Control 2024, 1–20. [Google Scholar] [CrossRef]
  49. Bravi, L.; Santos, G.; Pagano, A.; Murmura, F. Environmental management system according to ISO 14001: 2015 as a driver to sustainable development. Corp. Soc. Responsib. Environ. Manag. 2020, 27, 2599–2614. [Google Scholar] [CrossRef]
  50. Makhloufi, L.; Laghouag, A.A.; Meirun, T.; Belaid, F. Impact of green entrepreneurship orientation on environmental performance: The natural resource-based view and environmental policy perspective. Bus. Strategy Environ. 2022, 31, 425–444. [Google Scholar] [CrossRef]
  51. Li, H.; Lu, J. Temperature change and industrial green innovation: Cost increasing or responsibility forcing? J. Environ. Manag. 2023, 325, 116492. [Google Scholar] [CrossRef] [PubMed]
  52. Salehi, M.; Jalalian, M.; Siar, M.M.V. Green transportation scheduling with speed control: Trade-off between total transportation cost and carbon emission. Comput. Ind. Eng. 2017, 113, 392–404. [Google Scholar] [CrossRef]
  53. Wang, Z.; Wang, Q.; Zhang, S.; Zhao, X. Effects of customer and cost drivers on green supply chain management practices and environmental performance. J. Clean. Prod. 2018, 189, 673–682. [Google Scholar] [CrossRef]
  54. Sarkar, B.; Ullah, M.; Sarkar, M. Environmental and economic sustainability through innovative green products by remanufacturing. J. Clean. Prod. 2022, 332, 129813. [Google Scholar] [CrossRef]
  55. Gast, J.; Gundolf, K.; Cesinger, B. Doing business in a green way: A systematic review of the ecological sustainability entrepreneurship literature and future research directions. J. Clean. Prod. 2017, 147, 44–56. [Google Scholar] [CrossRef]
  56. Almalki, F.A.; Alsamhi, S.H.; Sahal, R.; Hassan, J.; Hawbani, A.; Rajput, N.; Saif, A.; Morgan, J.; Breslin, J. Green IoT for eco-friendly and sustainable smart cities: Future directions and opportunities. Mob. Netw. Appl. 2023, 28, 178–202. [Google Scholar] [CrossRef]
  57. Bataineh, M.J.; Sánchez-Sellero, P.; Ayad, F. Green is the new black: How research and development and green innovation provide businesses a competitive edge. Bus. Strategy Environ. 2024, 33, 1004–1023. [Google Scholar] [CrossRef]
  58. Gupta, H.; Kumar, A.; Wasan, P. Industry 4.0, cleaner production and circular economy: An integrative framework for evaluating ethical and sustainable business performance of manufacturing organizations. J. Clean. Prod. 2021, 295, 126253. [Google Scholar] [CrossRef]
  59. Le, T.T. The association of corporate social responsibility and sustainable consumption and production patterns: The mediating role of green supply chain management. J. Clean. Prod. 2023, 414, 137435. [Google Scholar] [CrossRef]
  60. Valero-Gil, J.; Surroca, J.A.; Tribo, J.A.; Gutierrez, L.; Montiel, I. Innovation vs. standardization: The conjoint effects of eco-innovation and environmental management systems on environmental performance. Res. Policy 2023, 52, 104737. [Google Scholar] [CrossRef]
  61. Ferrari, A.M.; Volpi, L.; Settembre-Blundo, D.; García-Muiña, F.E. Dynamic life cycle assessment (LCA) integrating life cycle inventory (LCI) and Enterprise resource planning (ERP) in an industry 4.0 environment. J. Clean. Prod. 2021, 286, 125314. [Google Scholar] [CrossRef]
  62. Kraus, S.; Jones, P.; Kailer, N.; Weinmann, A.; Chaparro-Banegas, N.; Roig-Tierno, N. Digital transformation: An overview of the current state of the art of research. Sage Open 2021, 11, 21582440211047576. [Google Scholar] [CrossRef]
  63. Papadopoulos, T.; Singh, S.P.; Spanaki, K.; Gunasekaran, A.; Dubey, R. Towards the next generation of manufacturing: Implications of big data and digitalization in the context of industry 4.0. Prod. Plan. Control 2022, 33, 101–104. [Google Scholar] [CrossRef]
  64. Oubrahim, I.; Sefiani, N.; Happonen, A. The influence of digital transformation and supply chain integration on overall sustainable supply chain performance: An empirical analysis from manufacturing companies in Morocco. Energies 2023, 16, 1004. [Google Scholar] [CrossRef]
  65. Zhu, Y. Research on evolutionary game of digital twin data information sharing based on blockchain technology. Meas. Control 2024, 00202940241245035. [Google Scholar] [CrossRef]
  66. Bhattacharya, S.; Chatterjee, A. Digital project driven supply chains: A new paradigm. Supply Chain Manag. Int. J. 2022, 27, 283–294. [Google Scholar] [CrossRef]
  67. Teece, D.J. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strateg. Manag. J. 2007, 28, 1319–1350. [Google Scholar] [CrossRef]
  68. Okorie, O.; Russell, J.; Cherrington, R.; Fisher, O.; Charnley, F. Digital transformation and the circular economy: Creating a competitive advantage from the transition towards Net Zero Manufacturing. Resour. Conserv. Recycl. 2023, 189, 106756. [Google Scholar] [CrossRef]
  69. Shi, H.; Feng, T.; Zhu, Z. The impact of big data analytics capability on green supply chain integration: An organizational information processing theory perspective. Bus. Process Manag. J. 2023, 29, 550–577. [Google Scholar] [CrossRef]
  70. Cuypers, I.R.; Hennart, J.-F.; Silverman, B.S.; Ertug, G. Transaction cost theory: Past progress, current challenges, and suggestions for the future. Acad. Manag. Ann. 2021, 15, 111–150. [Google Scholar] [CrossRef]
  71. Haque, F.; Fan, C.; Lee, Y.-Y. From waste to value: Addressing the relevance of waste recovery to agricultural sector in line with circular economy. J. Clean. Prod. 2023, 415, 137873. [Google Scholar] [CrossRef]
  72. Jell-Ojobor, M.; Raha, A. Being good at being good—The mediating role of an environmental management system in value-creating green supply chain management practices. Bus. Strategy Environ. 2022, 31, 1964–1984. [Google Scholar] [CrossRef]
  73. Hrouga, M.; Sbihi, A.; Chavallard, M. The potentials of combining Blockchain technology and Internet of Things for digital reverse supply chain: A case study. J. Clean. Prod. 2022, 337, 130609. [Google Scholar] [CrossRef]
  74. Cheng, Z.; Zeng, M. Heterogeneous hybrid governance mechanisms of information technology and contract in supply chain quality management. Manag. Decis. Econ. 2024, 45, 1290–1308. [Google Scholar] [CrossRef]
  75. Dillman, D.A. Procedures for conducting government-sponsored establishment surveys: Comparisons of the total design method (TDM), a traditional cost-compensation model, and tailored design. In Proceedings of the American Statistical Association, Second International Conference on Establishment Surveys, Buffalo, NY, USA, 17–21 June 2000; pp. 343–352. [Google Scholar]
  76. Xie, X.; Huo, J.; Zou, H. Green process innovation, green product innovation, and corporate financial performance: A content analysis method. J. Bus. Res. 2019, 101, 697–706. [Google Scholar] [CrossRef]
  77. Byrne, B.M.; Shavelson, R.J.; Muthén, B. Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychol. Bull. 1989, 105, 456–466. [Google Scholar] [CrossRef]
  78. Bonett, D.G.; Wright, T.A. Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. J. Organ. Behav. 2015, 36, 3–15. [Google Scholar] [CrossRef]
  79. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  80. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  81. Armstrong, J.S.; Overton, T.S. Estimating nonresponse bias in mail surveys. J. Mark. Res. 1977, 14, 396–402. [Google Scholar] [CrossRef]
  82. de Jong, P.F. Hierarchical regression analysis in structural equation modeling. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 198–211. [Google Scholar] [CrossRef]
  83. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Routledge: London, UK, 2013. [Google Scholar]
Sustainability 16 05989 g001
Figure 1. Research model. Notes: independent variables: supplier integration, customer integration; dependent variable: environmental cost performance; moderating variable: digital transformation.
Figure 1. Research model. Notes: independent variables: supplier integration, customer integration; dependent variable: environmental cost performance; moderating variable: digital transformation.
Sustainability 16 05989 g001
Sustainability 16 05989 g002aSustainability 16 05989 g002b
Figure 2. (A). Two-way interactions; (B). Two-way interactions. Notes: SI (supplier integration); CI (customer integration); EC (environment cost performance); DT (digital transformation).
Figure 2. (A). Two-way interactions; (B). Two-way interactions. Notes: SI (supplier integration); CI (customer integration); EC (environment cost performance); DT (digital transformation).
Sustainability 16 05989 g002aSustainability 16 05989 g002b
Table 1. The sample demographics (N = 311).
Table 1. The sample demographics (N = 311).
FrequencyPercentage
Industry sectorTextile6320.3
Furniture299.30
Chemicals, Pharmaceutical7825.1
Automobile206.40
Electric machinery and equipment9831.5
Others237.40
Firm size<5007223.2
500–10007925.4
1000–20006821.9
>20009229.6
Annual sales (hundred million RMB)<300227.10
300–5005818.6
500–10009931.8
>100013242.4
Investment in environmental products (Million RMB)<507223.2
50–1006520.9
100–5009831.5
>5007624.4
Firm age<1 year old20.60
1–5 years old3711.9
6–10 years old7323.5
11–20 years old12740.8
>20 years old7223.2
Sources: Authors work.
Table 2. Measurement items and validity assessment.
Table 2. Measurement items and validity assessment.
Overall Model Fit: χ2/df = 1.492; p < 0.01; CFI = 0.979; IFI = 0.978; RMSEA = 0.031
VariablesMeasurement ItemsSFLSEαCRAVE
Supplier IntegrationOur firm shares environmental information (e.g., emission reduction technology) with key suppliers.0.812 0.8460.8470.613
Our firm collaboratively anticipates and resolves environment-related problems with key suppliers.0.8380.069
Our firm makes joint decisions with key suppliers about the ways to reduce overall environmental impact of its activities.0.8340.072
Our firm couples its environmental management system with that of key suppliers.0.8270.073
Customer IntegrationOur firm shares environmental protection information with key customers.0.7680.0720.8790.8790.648
Our firm collaboratively anticipates and resolves environment-related problems with key customers.0.7790.073
Our firm makes joint decisions with key customers about the ways to reduce the overall environmental impact of its activities.0.810
Our firm couples its environmental management system with that of key customers.0.8260.072
Digital TransformationOur firm has been particularly advocating for digital transformation within the supply chain. 0.7910.0630.9100.9110.694
The adoption of digital technologies enables our firm to reduce pollution. 0.8420.076
Information sharing among supply chains has already been achieved through digital technologies. 0.784
Our firm has strengthened relationship management with suppliers through digital technologies.0.8610.083
Our firm has strengthened relationship management with customers through digital technologies.0.7880.092
Our firm has been particularly advocating for digital transformation within the supply chain. 0.7920.083
Environmental Cost PerformanceOur firm has reduced waste disposal costs.0.8160.0770.8770.8790.655
Our firm has decreased the total amount of fuel used for product/service transportation.0.8220.064
Our firm has reduced the consumption of energy and materials.0.8310.073
Our firm has minimized the use of hazardous materials in manufacturing products/services.0.799
Our firm has reduced waste disposal costs.0.8040.086
Notes: 1. One item from outcome control was removed after CFA due to its slower factor loading. 2. All standardized factor loadings are significant at 0.01. Sources: Authors work.
Table 3. Correlation matrix and descriptive statistics.
Table 3. Correlation matrix and descriptive statistics.
ConstructsMeanSD1234
Supplier Integration5.7540.922 0.3760.1450.171
Customer Integration5.9180.9030.613 ** 0.1250.166
Digital Transformation5.5440.9010.381 **0.353 ** 0.123
Environmental Cost Performance5.7590.9170.413 **0.407 **0.351 **
Notes: ** is significant at 0.01; Correlations are below the diagonal land squared correlations are above the diagonal. Sources: Authors work.
Table 4. Hypothesis testing results.
Table 4. Hypothesis testing results.
HypothesesRelationshipsProposed EffectResultSig
H1Supplier Integration → Environment Cost Performance+Supported0.000
H2Customer Integration → Environment Cost Performance+Supported0.000
H3aDigital Transfromation Moderation in H1+Supported0.000
H3bDigital Transfromation Moderation in H2+Supported0.000
Table 5. Hierarchical regression analyses.
Table 5. Hierarchical regression analyses.
Environmental Cost Performance
Model 1Model 2Model 3Model 4
ConstructsβVIFβVIFβVIFβVIF
Control variables
Textile0.0463.087−0.1873.014−0.1623.351−0.1513.433
Furniture0.1332.197−0.0682.451−0.0472.157−0.0872.146
Chemicals
Pharmaceutical		0.1273.245−0.1733.654−0.0583.495−0.1393.243
Automobile0.1221.754−0.0511.813−0.0991.824−0.1781.856
Electric machinery
and equipment		0.1623.821−0.0924.087−0.0814.165−0.1964.134
firm size0.0901.0350.0711.451−0.0781.167−0.0541.187
annual sales−0.0221.145−0.184 ***1.654−0.1921.458−0.232 *1.396
investment in
environmental products		0.0781.5460.0471.6810.0771.0360.0371.077
firm age0.0681.4580.0961.4290.0731.3780.0571.493
Predictors
Supplier Integration 0.407 ***1.1480.372 ***2.1980.341 ***2.228
Customer Integration 0.394 ***1.3160.358 ***1.9270.326 ***2.174
Moderator
Digital Transformation 0.356 ***1.3670.376 **1.691
Interaction effects
Supplier Integration × Digital Transformation 0.362 ***2.577
Customer Integration × Digital Transformation 0.394 ***1.295
R20.0360.4720.5610.598
Adjusted R20.0120.4630.5580.572
F change1.231237.51213.0497.842
Notes: *** p < 0.001, ** p < 0.01, * p < 0.05. Sources: Authors work.
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.

Share and Cite

MDPI and ACS Style

Li, J.; Zhong, D.; Ru, H.; Jia, L. Assessing the Nexus between Supplier and Customer Integration and Environmental Cost Performance: Insights into the Role of Digital Transformation. Sustainability 2024, 16, 5989. https://doi.org/10.3390/su16145989

AMA Style

Li J, Zhong D, Ru H, Jia L. Assessing the Nexus between Supplier and Customer Integration and Environmental Cost Performance: Insights into the Role of Digital Transformation. Sustainability. 2024; 16(14):5989. https://doi.org/10.3390/su16145989

Chicago/Turabian Style

Li, Jianwei, Deyu Zhong, Haoyu Ru, and Lixia Jia. 2024. "Assessing the Nexus between Supplier and Customer Integration and Environmental Cost Performance: Insights into the Role of Digital Transformation" Sustainability 16, no. 14: 5989. https://doi.org/10.3390/su16145989

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop