Comparing the Impact of Green Supplier Selection and Integration on Environmental Performance: An Analysis of the Moderating Role of Government Support

Abstract

As the green market becomes increasingly saturated, companies must allocate limited resources to more precise and efficient projects. This study aims to explore and compare the impact of green supplier selection and green supplier integration on environmental performance, with a particular focus on the moderating role of government support. The research was conducted through a survey of 391 Chinese manufacturing firms by a specialized research institution, employing hierarchical regression analysis. The results indicate that both green supplier selection and integration have a positive impact on environmental performance, with green supplier integration having a more significant effect. Moreover, active government support weakens the relationship between green supplier selection and environmental performance but strengthens the relationship between green supplier integration and environmental performance. Given the study’s context, scope, and sample size, certain limitations exist. This research highlights more strategically significant supplier management practices and emphasizes the moderating role of government support in different contexts, while also providing valuable and practical recommendations for relevant practitioners.

1. Introduction

Green supplier management is a key strategy for implementing a sustainable supply chain and acts as a core driver in operations and supply chain management (OSCM), especially for green supply chain management (GSCM) [1]. The selection and integration of suppliers are critical because choosing green suppliers ensures the environmental compliance of products at the source, which effectively reduces subsequent carbon emissions and waste disposal costs [2,3]. Green supply chain integration facilitates the efficient transfer of green resources among supply chain members, enhancing the synergy of green information, technology, and capabilities [4]. This, in turn, drives reductions in energy and material consumption and lowers environmental costs, thus playing a crucial role in GSCM [5]. Research on GSCM indicates that green supplier selection reduces the negative environmental impacts of manufacturing operations and improves sustainability through the exploration, evaluation, certification, and management of suppliers’ environmental dimensions [6,7]. Additionally, green supplier integration significantly reduces pollution levels, waste emissions, energy consumption, and overall environmental impact by leveraging suppliers’ green technologies and resources [8]. Green supplier integration involves joint production planning with suppliers, sharing information resources, and collaboratively addressing environmental issues, ultimately leading to improved resource allocation efficiency and reduced environmental costs. Consequently, more firms are implementing green supplier management to achieve a sustainable competitive advantage.

In the broader market environment, emphasizing the triple bottom line and sustainable development, more firms are adopting green practices, green innovation, and environmental programs to comply with increasingly stringent environmental policies and enhance the likelihood of long-term business success [9]. Moreover, as manufacturing is considered one of the most polluting industries, the emergence of related environmental regulations has compelled many manufacturing firms to focus on their environmental performance, whether voluntarily or by necessity. Consequently, contemporary research and practice are increasingly focusing on green practices and environmental issues [5,9]. Although numerous studies examine the positive impact of green supplier selection or green supplier integration on environmental performance [2,8], there remains a scarcity of research directly comparing the relative effects of these two strategies. A thorough literature search in databases such as Web of Science and Scopus reveals that existing studies predominantly focus on the independent impact of single strategies [10,11], lacking systematic comparisons of their effects on environmental performance. This indicates that within the field of Green Supply Chain Management (GSCM), the relative contributions and interaction mechanisms between green supplier selection and green supplier integration have not been adequately explored or validated. Therefore, this study aims to fill this research gap by providing practical guidance for companies to more effectively enhance their environmental performance and offering new theoretical perspectives for the academic community. The significance of this study lies in its potential to not only enrich the existing GSCM theoretical framework but also to provide a basis for companies to make more informed decisions in green supply chain management, particularly in terms of resource allocation and strategy selection. Green supplier selection and green supplier integration are two concepts that share the same goal but differ in process; both aim to promote corporate sustainability and enhance GSCM performance. Green supplier selection involves strategically evaluating and selecting suppliers who excel in environmental protection and green performance during the procurement process, ensuring the environmental performance of manufacturing firms [2]. In contrast, green supplier integration refers to strategic collaboration with suppliers, working together through practices such as sharing environmental information and jointly addressing environmental issues to improve both parties’ environmental performance [8]. Both supplier selection and integration require substantial resource investments for strategic deployment [12]. For instance, supplier selection entails transaction costs (e.g., exploration and negotiation costs) [10], while supplier integration requires various resources (e.g., economic and cooperative resources) [13]. This study aims to compare the impact of green supplier selection and green supplier integration on environmental performance and explore the complex relationship between them, seeking more effective ways to enhance corporate environmental performance and promote sustainable development, thereby filling the research gap in this area.

Additionally, the study needs to consider external contextual factors beyond the control of firms, particularly government support. Given this research’s focus on Chinese manufacturing firms, China’s significance in the global manufacturing sector, and the Chinese government’s emphasis on environmental protection and related policy support, government support as a moderating variable can significantly interact with green supplier selection and green supplier integration. This study aims to explore and reveal the complex interrelationships within the context of Chinese manufacturing, offering valuable insights for organizations seeking to enhance environmental performance and supply chain sustainability.

This study has seven sections. The research background, objectives, and needs of this study are outlined in Section 1. The Section 2 includes a review of the literature and the development of a hypothesis. In this section, theoretical discussions of the four variables in the structural equation model are elaborated, and their complex relationships are examined. In Section 3, the research methods are explained, including how data was collected, reliability and validity analyses, model fit assessment, and other preliminary validation tasks before regression analysis. The regression results are presented in Section 4, which confirms and supports the hypotheses. In Section 5, the discussion examines the theoretical and practical implications of the research results. The study’s limitations are summarized in Section 6, and there are suggestions for further research. Finally, the references are listed in the last section.

2. Literature Review and Hypothesis Development

2.1. Green Supplier Selection

Green supplier selection is a key strategy for achieving sustainable supply management, focusing on the green capabilities of suppliers [14]. Unlike traditional supplier selection, green supplier selection emphasizes not only traditional competitive advantages such as product quality, delivery speed, cost-effectiveness, and responsiveness but also prioritizes the green capabilities of suppliers, environmental compliance, and product sustainability [15]. By effectively evaluating suppliers from an environmental perspective, firms can strategically respond to increasingly stringent environmental regulations and the expanding consumer need for ecologically friendly, green products [16]. Previous supply chain management literature has investigated the positive effects of supplier selection across various dimensions, including the economic dimension [17], the innovation dimension [18], the risk management dimension [19], and the environmental dimension [20,21]. As the upstream segment of the supply chain, suppliers are fundamental in determining product attributes and significantly impact product quality levels [22]. Additionally, as market competition intensifies and uncertainty increases, it becomes challenging for firms to achieve their business goals independently [23]. Consequently, more firms are opting to collaborate with external partners to maintain competitiveness and enhance their competitive advantage [7].

This study specifically focuses on the green environmental and sustainability dimension of supplier selection and examines the environmental dimension of green supplier selection inside the expanded resource-based view (RBV) theoretical framework [24].

2.2. Green Supplier Integration

In contemporary supply chain management literature, supplier integration is a well-established concept [25]. Firms engage in strategic cooperation with key external suppliers, leveraging valuable information acquisition, the sharing of scarce knowledge, and the provision of unique technical support [26]. These resource-sharing practices can result in efficient synergistic effects, stable risk management, and flexible dynamic capabilities, among other positive impacts [27]. Green supplier integration builds on this foundation by specifically targeting suppliers who possess environmental and sustainable development capabilities. Previous literature indicates that manufacturing firms engage in structured, inter-organizational strategies, practices, and processes with green suppliers to create coordinated and synchronized workflows [28,29]. This integration allows firms to harness the green information, knowledge, technology, and resources that suppliers offer, thereby optimizing resource allocation and achieving cost savings in environmental management [30]. Furthermore, successful integration fosters closer relationships between firms and suppliers, which is a critical prerequisite for realizing the win-win logic of both green and economic benefits [31].

Additionally, successful green supply chain integration not only enhances a firm’s environmental performance but also helps build a socially responsible green image [32]. This, in turn, can increase consumer and stakeholder loyalty and improve the firm’s green reputation [33]. Such an image can elevate the manufacturing firm’s market position and establish a distinct competitive advantage in terms of environmental performance [34].

2.2.1. Environmental Performance

Environmental performance refers to the comprehensive assessment of a firm’s green practices, including the control of harmful raw materials, levels of pollution emissions, energy consumption, and occurrences of environmental incidents [35,36]. In today’s business environment, which increasingly emphasizes sustainable development, the triple bottom line (ESG) is considered a key strategy for creating competitive advantage and achieving better economic performance [37]. The environmental dimension, in particular, is increasingly recognized by supply chain managers as a critical means to foster competitive advantage by creating green reputations and establishing eco-friendly images [38].

As such, the literature on supply chain management, particularly green supply chain management(GSCM), concentrates on incorporating the environmental aspect into supply chain operations [39], green innovation (GI) [40], environmental management systems (EMS) [41], and environmental performance assessment (EPA) [42]. These studies provide valuable evidence of the positive impacts of the environmental dimension. For instance, early research by Hart and Dowell [43] found that companies can enhance profitability by reducing pollution levels, as this can lower waste disposal costs and reduce expenses related to environmental compliance. Subsequently, Miao et al. [44] discovered that improving environmental performance can promote resource use efficiency and save environmental costs [45]. More recently, Opazo-Basáez et al. [46] proposed that green innovation technologies may be driven by strong environmental performance, giving firm a competitive edge in terms of consumers’ green values. The purpose of this research is to highlight the significance of environmental performance and investigate it using the extended resource-based view (ERBV) theory as a framework.

2.2.2. The Main Effect on ERBV Framework

The Resource-Based View (ERBV) is a well-established theoretical perspective in strategic management, emphasizing that firms build competitive advantages through internal resources that are valuable, rare, inimitable, and non-substitutable (VRIN) [47]. However, [48] highlighted certain omissions in the RBV theory. With the continuously changing and increasingly uncertain external market environment, traditional resource capabilities are insufficient to fully explain organizational competitive advantages [49]. This led to the development of the Extended Resource-Based View (ERBV). ERBV posits that firms should not limit themselves to internal resources but should also seek external resources, particularly those obtained through collaboration with partners [24]. Such cross-boundary cooperation is considered an effective way to acquire strategic resources. This study positions green supplier selection and integration as key factors influencing environmental performance and explains their relationship within the ERBV framework.

Firstly, selecting green suppliers involves evaluating the green performance of multiple suppliers in the market and choosing those best suited to complement a firm’s resource deficiencies [7]. The selected suppliers typically possess advanced environmental technologies and comprehensive environmental management systems [50]. This can help manufacturing firms reduce the negative environmental impact of their products and effectively control pollution levels [51]. For example, Apple collaborates with its supplier “Bluesign” to ensure that textiles and electronic components in its supply chain strictly comply with environmental standards. This initiative helps Apple reduce the use of harmful chemicals and optimize resource use efficiency. Additionally, choosing green suppliers that use recyclable or reusable packaging can help reduce energy and material consumption, achieving both green and economic objectives for both parties [52]. For instance, Nike collaborates with green suppliers to use environmentally friendly materials for packaging. Nike’s shoeboxes are made from 100% recycled paper, and the optimized design reduces the amount of packaging material used [53]. This not only lowers material costs but also reduces waste generation [54]. Such initiatives can enhance a brand’s green image and reputation while achieving both economic and environmental benefits [55]. Therefore, within the ERBV framework, green supplier selection is crucial for manufacturing firms to enhance their environmental performance. By selecting suppliers with green capabilities, firms can gain abundant green resources, thus promoting sustainable competitive advantages [7]. Hence, this study proposes Hypothesis 1:

H1. 

Green supplier selection positively impacts environmental performance.

Secondly, green supplier integration can help firms establish environmentally focused collaborative relationships with their suppliers [56]. In such relationships, firms can acquire green resources from suppliers, such as environmentally friendly technologies, green knowledge, and pollution control capabilities [3]. By combining internal resources with external ones, firms can optimize their green production processes [57]. The green collaboration experience gained through working with suppliers can be applied to every link in the supply chain. For instance, integrating the environmental management systems of a firm with its key suppliers can enhance the alignment of green processes and generate green operational expertise [58]. Firms can adapt and apply this expertise throughout the supply chain, creating a positive cycle and achieving overall green supply chain optimization [59]. This not only improves a firm’s environmental performance but also helps the entire supply chain achieve green management.

Moreover, firms and suppliers sharing environmental information and collaborating to predict and address environmental issues contribute to the development and innovation of more eco-friendly products and production processes [60]. Green suppliers typically possess advanced environmental management resources, and firms can integrate these resources with their own to develop strategic plans that effectively enhance environmental performance [61]. Therefore, within the ERBV framework, green supplier integration can help firms acquire ample green resources from their suppliers, thereby significantly improving environmental performance [56]. Thus, this study proposes Hypothesis 2:

H2. 

Green supplier integration positively impacts environmental performance.

2.3. The Moderative Effect of Government Support

This study aims to explore the multifaceted relationships between green supplier selection, green supplier integration, and environmental performance, examining their direct impacts and the complex interactions under the influence of government support as key institutional factors. The study adopts a contingency theory perspective, which posits that no single management strategy is effective in all situations and emphasizes that organizations need to manage behavior and allocate resources based on different, specific, and unique contexts [62]. This study aims to reveal how the effectiveness of green supplier selection and integration in promoting environmental performance varies under these contextual conditions.

Contingency theory suggests that manufacturing firms operate in diverse contextual environments, and their decision-making behavior is influenced by their unique circumstances. The theory includes formal contexts (characterized by clear features and norms) [63] and informal contexts (involving organizational culture, partnerships, and informal communication) [64]. Government support is considered a formal context, understood as the encouragement and assistance provided by the government for firms’ environmental operations through relevant policies [65]. This includes various forms of aid to manufacturing firms, such as tax incentives, technical assistance, R&D funding, and environmental certifications [66]. In this context, firms that receive such additional resources may reduce their dependence on suppliers, as the government already provides sufficient green technology and resource support [67]. Reliance on government support can decrease the motivation of firms to select green suppliers, as firms may feel that existing government support adequately meets their environmental needs [68]. Furthermore, existing research has sufficiently demonstrated that government support can positively impact environmental performance [69,70]. When firms achieve significant environmental performance with government support, they may be reluctant to invest additional resources in evaluating new green suppliers, further reducing the motivation to select green suppliers. For example, Huawei has been heavily reliant on green suppliers in its supply chain, selecting those that meet environmental standards to reduce the carbon footprint of its products. However, due to substantial support from the Shenzhen municipal government, including significant environmental technology subsidies and access to government-provided green technologies, Huawei has been able to enhance the environmental performance of its products without relying extensively on external green suppliers. While this government support has improved Huawei’s overall environmental capabilities, it may also have diminished the firm’s motivation to proactively select green suppliers. Additionally, in the presence of ample government support, firms’ resource allocation and decision-making may lean towards directly utilizing government-provided resources rather than indirectly enhancing environmental performance through the selection of green suppliers. This decision-making conflict can lead firms to strategically prefer utilizing existing government support over actively investing in green supply chain construction. Therefore, although government support can independently enhance environmental performance, its interaction with green supplier selection may negatively impact environmental performance due to resource dependency and decision-making conflicts. Therefore, this study proposes Hypothesis 3a:

H3a. 

Government support negatively moderates the relationship between green supplier selection and environmental performance.

In contrast to its negative impact on green supplier selection, government support may positively influence the integration of green suppliers. Unlike supplier selection, supplier integration involves strategic collaboration between firms and their key suppliers to jointly determine how to reduce the overall environmental impact of their activities [71]. This deep level of resource integration and technology sharing can help firms and green suppliers create synergistic effects, that are not attainable through green supplier selection alone [3]. This difference arises because green supplier selection and green supplier integration differ in their operational nature and the depth of their strategic collaboration.

Within the context of collaboration, government support can provide manufacturing firms and green suppliers with more resources and opportunities for deep cooperation [34]. This support fosters in-depth exchanges in areas such as green technology, green information, and green resources, encompassing multiple dimensions including green products, green process innovation, green production, and sustainable development [60,72]. For example, BYD has deeply integrated green technologies with its suppliers in the production of batteries for electric vehicles. BYD has not only collaborated with suppliers to develop environmentally friendly materials but has also benefited from research and development funding and policy incentives provided by the Chinese government. This government support has facilitated resource sharing and technological integration between BYD and its suppliers, enabling BYD to effectively reduce carbon emissions during the production process and enhancing the company’s competitive advantage in green technology. Therefore, in this context, firms will fully utilize the resources provided by government support to expand their cooperation with green suppliers and invest more resources into integration, thereby more effectively promoting and enhancing environmental performance. Therefore, this study proposes Hypothesis 3b:

H3b. 

Government support positively moderates the relationship between green supplier integration and environmental performance.

3. Research Method

3.1. Overview of Research Methods

Structural Equation Modeling (SEM) was used in this work to examine the research model because it enables the evaluation of intricate correlations between observable variables and latent components (Figure 1). This approach works especially well when analyzing relationships between variables that are both direct and indirect. The purpose of the study model is to examine how government support, green supplier selection, green supplier integration, and environmental performance interact. It also looks into how green supplier selection and green supplier integration directly affect environmental performance and how government support influences these interactions in a moderating way.

The study first operationally established the measuring items for each variable based on previous research in order to evaluate these associations (for more information, see Section 3.3). Next, a survey questionnaire was created in order to gather information. The process of gathering data was then carried out (for more information, see Section 3.2). Following the completion of data collection, countermeasures and statistical techniques were implemented to address prevalent approach bias (see Section 3.5 for more). Confirmatory Factor Analysis (CFA) was conducted using AMOS 23.0 to evaluate the model fit and the assessment items’ unidimensionality (see Section 3.4 for more). Lastly, the hypotheses were tested using SPSS and hierarchical regression analysis (see Section 4 for specifics).

In conclusion, this study carefully considered the proposed research model and validated the connections between green supplier integration, government assistance, environmental performance, and green supplier selection using robust methodological techniques.

3.2. Data-Collection Procedures

In the framework of China’s manufacturing industry, this study primarily investigates the linkages between government support, green supplier selection, and green supplier integration with environmental performance. The industries included in the research include textile, furniture, chemical, pharmaceutical, automobile, and electrical equipment manufacturing. To guarantee accuracy and content validity, the research team first created a structured questionnaire in English. This was later translated into another language by bilingual specialists. A pilot test was carried out prior to the questionnaire’s official distribution, and modifications were made in response to feedback. In order to make sure participants understood the contents of the questionnaire, researchers contacted them via WeChat and email.

The study employed systematic sampling to identify target firms across the Chinese manufacturing sector, categorized according to the Chinese industry codes: P89 (textiles), Y80 (furniture), C10 (pharmaceuticals and chemicals), T40 (automotive), and N20 (electrical equipment). This sampling method was chosen to ensure the sample’s structured and effective representativeness. Data collection commenced in September 2023 and spanned three months, focusing on 1600 Chinese manufacturing firms producing green products. The survey distribution followed Dillman [73] Total Design Method (TDM), with reminder emails sent biweekly, totaling three reminders. Out of the 1600 firms contacted, 965 agreed to participate and provided contact information, but ultimately, only 311 respondents completed the survey, resulting in a response rate of 19.44%. Of the 311 responses collected, 18 were deemed invalid, leaving 293 valid responses for analysis. To address the issue of low response rates and sampling bias, in July 2024, this study conducted follow-up outreach to the 965 companies that had initially agreed to participate but had not completed the survey. As a result of this follow-up, the final valid sample size increased to 391 respondents. Subsequently, the data was reanalyzed to ensure the robustness of the findings; the final response rate is 24.44%.

Table 1. The sample demographics (N = 391).

		Frequency	Percentage
Industry sector	Textile	69	17.6
	Furniture	61	15.6
	Chemicals, Pharmaceutical	78	19.9
	Automobile	29	07.4
	Electric machinery and equipment	95	24.3
	Others	59	15.2
Firm size (the numbers of employees)	<500	214	54.7
	500–1000	63	16.1
	1000–2000	71	18.1
	>2000	43	11.1
Annual sales (hundred million RMB)	<300	26	06.6
	300–500	88	22.5
	500–1000	165	42.2
	>1000	112	28.7
Investment in environmental (Million RMB)	<50	139	35.5
	50–100	75	19.2
	100–500	168	42.9
	>500	103	26.4
Firm age	<1 year old	8	02.2
	1–5 years old	42	10.7
	6–10 years old	109	27.8
	11–20 years old	153	39.1
	>20 years old	79	20.2

Source(s): Author’ own work.

details the demographic characteristics of these firms, including firm type, age, size, and annual sales revenue.

3.3. Measurement Items

The construction of

Table 2. Measurement items and validity assessment.

Overall Model Fit: χ2/df = 2.069; p < 0.01; CFI = 0.977; IFI = 0.978; RMSEA = 0.038
Variables	Measurement Items	SFL	SE	α	CR	AVE
Green Supplier Selection [10]	Our firm invests in selecting green suppliers whose processes/products are environmentally safe	0.789		0.889	0.890	0.613
	Our firm invests in selecting green suppliers who use recyclable/reusable packaging	0.803	0.075
	Our firm invests in selecting green suppliers who participate in green purchasing initiatives	0.799	0.074
	Our firm selects green suppliers that create as little waste as possible	0.812	0.072
Greem Supplier Integration [8]	Our firm shares environmental information (e.g., emission reduction technology) with key suppliers	0.801		0.910	0.910	0.648
	Our firm collaboratively anticipates and resolves environment-related problems with key suppliers	0.809	0.062
	Our firm makes joint decisions with key suppliers about the ways to reduce overall environmental impact of its activities	0.796	0.081
	Our firm couples its environmental management system with that of key suppliers	0.831	0.053
Government Support [75]	Chinese government implements policies and programs that are beneficial to our firm’s environmental operations.	0.845		0.908	0.908	0.685
	Chinese government provides needed technology information and technical support to our firm’s environmental operations.	0.862	0.063
	Chinese government plays a significant role in providing financial support for our firm’s environmental operations.	0.881	0.063
	Chinese government helps our firm obtain license for imports of technology, manufacturing, and other equipment needed to our firm’s environmental operations.	0.874	0.061
Environmental Performance [74]	Our company reduces pollution.	0.810		0.913	0.912	0.588
	Our company reduces waste and emissions (such as air emissions, wastewater, and solid waste).	0.832	0.071
	Our company has reduced the negative impact of our products on the environment.	0.793	0.074
	Our company has reduced the consumption of hazardous/harmful/toxic materials.	0.778	0.068
	Our company is reducing energy and material consumption.	0.762	0.071
	Our company has reduced the frequency of environmental accidents.	0.759	0.071

Note(s): 1. One item from outcome control was removed after CFA due to it slower factor loading 2. All standardized factor loadings are significant at 0.01. Source(s): Author’ own work.

in this study involved a thorough examination of prior research and theoretical underpinnings. Key constructs are operationalized through the use of survey items, which guarantees their validity and applicability in the context of the research. Green supplier selection employs four items from Anvarjonov, Um, Zhong and Shine [10], evaluating the extent to which participants’ companies invest in selecting environmentally safe suppliers, use recyclable packaging, participate in green procurement programs, and minimize waste. Green supplier integration uses four measurement items from Wong, Wong and Boon-itt [8], assessing the degree of cooperation between participants’ companies and their primary suppliers on environmental dimensions. Environmental performance is based on six measurement items from Roh et al. [74], evaluating the extent of participants’ companies’ efforts to reduce waste and emissions, and decrease energy and material consumption. Finally, government support includes four measurement items from Guo et al. [75], assessing the extent of support from the Chinese government for the participants’ companies’ environmental operations. All these items are measured using a seven-point Likert scale.

The research also included five control variables: industry sector (converted to dummy variables), average sales (log scale), firm age (log scale), investment in the environment (log scale), and firm size (log-transformed number of workers).

3.4. Construct Validity

To prepare for hypothesis testing, this study conducted a thorough Confirmatory Factor Analysis (CFA) using AMOS 22.0 to assess the unidimensionality of the measurement items. Compared to other techniques such as coefficient alpha and exploratory factor analysis [76], CFA is preferred because it provides a more rigorous assessment of unidimensionality. The results in

Table 3. Correlation matrix and descriptive statistics.

Constructs	Mean	SD	1	2	3	4
Green Supplier Selection	5.513	0.813		0.401	0.504	0.464
Green Supplier Integration	5.506	0.886	0.633 **		0.346	0.372
Government Support	5.610	0.831	0.710 **	0.588 **		0.326
Environment Performance	5.591	0.862	0.681 **	0.610 **	0.571 **

Note(s): ** is significant at 0.01; Correlations are below the diagonal and squared correlations are above the diagonal. Source(s): Author’ own work.

show good fit indices for all items: χ²/df = 2.069, RMSEA = 0.038, CFI = 0.977, and IFI = 0.978. These robust fit indices support the adoption of the proposed measurement model and confirm the unidimensionality of the measurement items.

This study further evaluated construct reliability using Cronbach’s alpha, with the obtained alpha values ranging from 0.889 to 0.913, exceeding the recommended threshold of 0.700 [77]. To further test validity, this study assessed convergent and discriminant validity. Convergent validity was established through standardized factor loadings (SFL) ranging from 0.759 to 0.881, exceeding the 0.5 threshold; composite reliability (CR) values for each construct ranged from 0.890 to 0.912, exceeding 0.700; and the average variance extracted (AVE) for each construct ranged from 0.588 to 0.685, exceeding the 0.500 benchmark [78].

Additionally, to test discriminant validity, this study conducted a correlation analysis (refer to Table 3). By comparing the square root of each construct’s AVE with the squared correlations between that construct and others, this study not only confirmed discriminant validity but also examined the interrelationships between the variables. Following the principles proposed by Fornell and Larcker [78], the lowest AVE value exceeded the highest squared correlation value, thereby confirming discriminant validity among the constructs tested.

3.5. Non-Response Bias and Common Method Bias

This study contrasted early and late responders taking into account firm size, industry sector, firm age, and annual sales in order to address potential non-response bias [79]. The lack of significant differences discovered by the analysis indicates that non-response bias is not a problem in this investigation. Furthermore, this study used three statistical techniques to reduce the bias associated with typical methodologies. The first step of component analysis identified three variables, the largest of which explained 36.851% of the variation overall, suggesting that common technique bias is not a significant problem. However, a subsequent confirmatory factor analysis showed that the fit indices of the original model (χ²/df = 2.069, RMSEA = 0.038, CFI = 0.977, and IFI = 0.978) and the extended model (χ²/df = 1.982, RMSEA = 0.041, CFI = 0.969, and IFI = 0.970) were similar. Third, this study utilized the X² Threshold in the Stats Tools Package to calculate the chi-square difference/degree of freedom difference and found that common method bias is not a significant issue in this study [80].

4. Hypothesis Results

This study created a structural equation model and used SPSS to do hierarchical regression analysis. Phased testing and variable grouping were used in a methodical manner to thoroughly assess the hypotheses. This statistical method was used to evaluate the degree to which fluctuations in continuous variables may be explained by strongly linked predictor variables [81]. In order to effectively investigate the effects of independent variables on the dependent variable and their interactions with moderating variables, the hierarchical method takes into account potential influences from various subgroups, allows for an in-depth exploration of the complex relationships between multiple variables, and controls for the effects of confounding variables. One way to depict the hierarchical regression model is as follows:

Y = β₀ + β₁ X₁ + β₂ X₂ + … + β_k X_k + ϵ

where Y is the dependent variable, X₁, X₂, …, X_k are the independent variables, β₀ is the intercept, β₁, β₂, …, β_k are the coefficients, and ϵ\epsilonϵ is the error term.

Main Effects Equation: EP = β₀ + β₁GSS + β₂GSI + β₃GS + ϵ

Interaction Effects Equation: EP = β₀ + β₁GSS + β₂GSI + β₃GS + β₄(GSS × GS) + β₅(GSI × GS) + ϵ

Note(s): Green Supplier Selection (GSS); Green Supplier Integration (GSI); Government Support (GS); Environmental Performance (EP).

This study created four models before moving on to the hypothesis testing stage (Please see

Table 4. Hierarchical regression analyses.

Constructs	Environmental Performance
	Model 1		Model 2		Model 3		Model 4
	β	VIF	β	VIF	β	VIF	β	VIF
Control variables
Textile	0.196 *	2.198	−0.103	2.877	−0.021	3.155	−0.016	3.008
Furniture	0.261 **	2.069	0.010	3.951	0.007	2.921	−0.192 *	2.432
Chemicals Pharmaceutical	0.281 **	1.486	0.013	3.054	−0.023	3.452	−0.045	2.313
Automobile	0.171 *	1.328	−0.009	2.943	0.007	1.891	−0.013	2.096
Electric machinery and equipment	0.286 **	2.495	0.021	3.132	0.013	3.762	−0.041	3.187
Firm size	0.167 *	1.482	0.019	1.541	0.022	1.033	0.003	1.458
Annual sales	0.029	1.367	0.035	1952	0.030	1.078	0.008	1.031
Investment in Environmental products	0.061	1.081	0.024	1.734	−0.019	1.011	0.021	1.078
Firm age	0.149	2.004	−0.026	2.912	−0.031	2.233	−0.048	2.945
Predictor
Green supplier selection			0.299 ***	2.478	0.305 ***	2.996	0.231 ***	2.125
Green supplier integration			0.420 ***	2.922	0.421 ***	2.781	0.391 ***	2.977
Moderators
Government support					0.173 *	2.115	0.161 *	2.881
Interaction effects
Green supplier selection× Government support							−0.211 **	2.157
Supplier integration× Government support							0.393 ***	2.330
R2	0.021		0.481		0.485		0.508
Adjusted R2	0.019		0.476		0.481		0.501
F change	3.481		349.710		15.146		24.128
Durbin-Watson							1.903

Note(s): *** p < 0.001, ** p < 0.01, * p < 0.05. Source(s): Author’ own work.

). To investigate the relationship between control factors and environmental performance, Model 1 (M1) only included control variables such as firm size, firm age, and industrial sector. The two primary predictor variables—green supplier integration and selection—were presented in Model 2 (M2). By adding government support as a moderating component, Model 3 (M3) expanded on Model 2. The interactions between the government support and the selection of green suppliers as well as the integration of green suppliers were incorporated into Model 4 (M4), which contained interaction effects. The following are the hierarchical regression formulae for the four models:

Model 1: EP = β₀ + β₁Control Variables + ϵ

Model 2: EP = β₀ + β₁Control Variables + β₂GSS + β₃GSI + ϵ

Model 3: EP = β₀ + β₁Control Variables + β₂GSS + β₃GSI + β₄GS + ϵ

Model 4: EP = β₀ + β₁Control Variables + β₂GSS + β₃GSU + β₄GS + β₅(GSS × GS) + β₆(GSI × GS) + ϵ

The results indicated significant statistical effects for furniture, chemicals and pharmaceuticals, automotive manufacturing, and electrical machinery in M1. In M2, the introduction of the predictor variable green supplier selection showed a significant relationship with environmental performance (β = 0.299, p < 0.000), supporting Hypothesis 1. Similarly, the predictor variable green supplier integration showed a significant relationship with environmental performance (β = 0.420, p < 0.000), supporting Hypothesis 2. M3 displayed the relationship between the moderating variable and the dependent variable: government support (β = 0.173, p < 0.05). In M4, the interaction effects were evident, with the interaction between green supplier selection and government support showing a statistically significant negative effect (β = −0.211, p < 0.000), while the interaction between green supplier integration and government support showed a statistically significant positive effect (β = 0.393, p < 0.000). Additionally, in the regression analysis of this study, the VIF values ranged from 1.011 to 3.951, indicating that multicollinearity is unlikely to be a significant issue.

To illustrate the moderating effects, this study graphically represented the relationships between green supplier selection and green supplier integration with environmental performance, considering high and low levels of government support. High and low values were defined as ±1 standard deviation from the mean Cohen [82]. Table 4 provides detailed regression results, and Figure 2a,b visually depict the moderating effects. In Figure 2a, the X-axis represents the increase in the independent variable, green supplier selection, while the Y-axis shows the corresponding changes in environmental performance. In Figure 2b, the X-axis represents the increase in the independent variable, green supplier integration, and the Y-axis shows the changes in environmental performance. The red and blue lines on the Z-axis indicate the levels of government support, with the red line representing high government support and the blue line representing low government support, offering an academic visualization of the interactions between the variables.

5. Discussions

5.1. Theory Integration

This study investigates the relationship between green supplier selection, integration, and the environmental performance of manufacturing firms by theoretically integrating the extended resource-based view (RBV) and contingency theory [24,62]. Before delving into the discussion, this study first reviews the contributions of ERBV and contingency theory. In recent years, more companies have criticized the internal focus of the RBV, emphasizing that competitive advantage requires the development of both internal and external assets [24]. This theory is known as the Extended Resource-Based View (ERBV) [49]. The ERBV theory has contributed to our understanding and explanation of achieving competitive advantage in a more integrated manner [83]. Although ERBV provides a rich perspective on understanding how firms gain competitive advantage, Beamish and Chakravarty [84] argue that ERBV may be insufficient in explaining multi-level analysis or more contextual backgrounds. They emphasize that the Resource-Based View needs to be integrated with other theoretical perspectives to comprehensively address its theoretical limitations [85,86], a viewpoint that aligns with ours. Therefore, this study introduces contingency theory to address the limitations of ERBV [62], as contingency theory offers multidimensional insights. For instance, Stonebraker and Afifi [87] suggest in their study that new perspectives can be developed through contingency theory. Our research contributes to the literature on theoretical integration by combining ERBV and contingency theory. Furthermore, our research contributes to the contingency theory literature, especially since the application of contingency theory has been relatively scattered in the past [88]. This study designates it as a contextual factor. On this basis, ERBV provides insights into how firms can enhance their competitive advantage by acquiring external resources, while contingency theory demonstrates the importance of different contextual factors influencing various mechanisms [89]. This theoretical review helps us consider the feasibility of theoretical integration [90]. In early studies on theoretical integration, Baard et al. [91] suggested that although different theoretical perspectives guide different research directions, integrating multiple theoretical perspectives facilitates a more comprehensive explanation of research phenomena [92]. Moreover, Markóczy and Deeds [93] proposed that interdisciplinary theoretical integration helps bring new perspectives and innovative thinking, a viewpoint later supported by Shaw et al. [94]. In conclusion, theoretical integration not only enriches our existing perspectives but also opens up many new directions and understandings [95].

5.2. Theoretical Implications

The extended RBV posits that firms must continuously acquire strategic resources from external sources to maintain competitiveness and enhance their competitive advantage. Conversely, contingency theory underscores the importance of firms adapting their strategies to contextual factors to ensure optimal decision-making.

The core of this study is to compare the impact levels of green supplier selection and green supplier integration on firms’ environmental performance. Drawing on the extended resource-based view (ERBV), this study examines the fundamental role of green suppliers in reducing pollution, energy consumption, waste discharge, and negative environmental impacts. Within this framework, this study proposeses echanisms through which manufacturing firms acquire green resources, such as green information, green technology, and green raw materials, from green suppliers to enhance their environmental performance. The empirical results support this hypothesis, indicating that manufacturing firms can enhance their environmental performance by establishing strategic partnerships with suppliers who possess green processes and environmentally friendly technologies. From an academic perspective, the findings resonate with the ERBV view, emphasizing that competitive advantage relies not only on internal resources but also on interactions and exchanges with external partners. Additionally, the results confirm the critical role and empirical evidence of green supplier selection in enhancing environmental performance, aligning with existing research. The positive impact of green supplier integration on environmental performance is significantly supported by this study. Zhang et al. [96] support this view through their research on the impact of green suppliers on environmental performance via environmental innovation, demonstrating a positive causal relationship. Similarly, Wiredu, Yang, Sampene, Gyamfi and Asongu [39] identified that incorporating environmental standards into supplier selection and evaluation, and maintaining cooperation with environmentally friendly suppliers, can significantly improve environmental performance. Thus, this study views green supplier selection as a strategic means to optimize firms’ environmental foundation, ensuring control over the use of hazardous, toxic, or harmful raw materials by selecting environmentally friendly suppliers. Moreover, green supplier integration is seen as a practice for acquiring new green resources and strengthening green competitive advantages. Firms can effectively reduce the overall negative environmental impact of their products, production processes, and operations by integrating environmental protection systems with green suppliers. Collaboratively predicting and addressing environmental issues with green suppliers can significantly enhance green levels, thereby gaining a competitive advantage in the environmental dimension. Therefore, this study not only validates but also extends and enriches the existing theoretical framework, revealing the crucial role of green supplier selection and integration in improving firms’ environmental performance.

Secondly, through the framework of contingency theory, this study explores the moderating effect of government support on the two main effects (i.e., the impact of green supplier selection and integration on environmental performance). The study elucidates that government support may have different moderating effects in various contexts. First, in the context of green supplier selection, government support, as an external factor beyond the control of firms, introduces a high degree of uncertainty. High levels of government support provide ample resources for firms’ environmental operations. This policy implementation, where firms can obtain resources with zero investment, might lead to over-reliance on government support, causing a loss of focus on green suppliers. Additionally, it could negatively impact the relationship between firms and suppliers. When government resource support is interrupted, firms may struggle to maintain their previous level of environmental performance. Furthermore, contingency theory emphasizes adaptability and innovation; excessive government support might hinder firms’ motivation for independent innovation, such as green product innovation and green process innovation, thereby negatively affecting environmental performance. In the context of green supplier integration, although government support is also an external factor, it plays a different role. When firms integrate green suppliers and establish strategic, mutually beneficial relationships with them, the environmental resources obtained from government support can be shared with suppliers. This resource sharing not only enhances both parties’ green capabilities and environmental performance but also strongly promotes a steadfast sustainable cooperation relationship. In adverse circumstances, such as government support interruptions or unfavorable situations for the firm, effective resource sharing can reduce suppliers’ opportunistic behavior. Based on the above results, this study concludes that the moderating role of government support in different contexts leads to variations in the relationship between green supplier selection and integration and environmental performance. The fundamental reason for this difference lies in the nature of the processes: green supplier selection is more of a static, one-way decision-making process, while green supplier integration is a dynamic, two-way cooperative process. With government support, the investment of resources and policies maximizes the synergistic effect between firms and suppliers, resulting in stronger environmental performance.

5.3. Practical Impalications

Our research not only explores theoretical implications but also provides several practical insights for leveraging strategic supplier management to enhance environmental performance in manufacturing firms. The results of this study highlight several practical implications. (1) Selecting green suppliers with outstanding environmental protection and green performance, and integrating green suppliers to establish resource synergy relationships, are key drivers in enhancing a firm’s environmental performance. In the context of manufacturing firms, sustainability and environmental protection are crucial. Especially under the current Chinese government policy that emphasizes “lucid waters and lush mountains are invaluable assets”, and with relevant government support for environmental operations, companies need to measure the progress of their green practices through their environmental performance. (2) Through green supplier selection, firms can ensure their supply chain complies with environmental regulations, effectively controls pollution levels, energy consumption, and the use of hazardous substances. By integrating green suppliers, firms can achieve deep collaboration with suppliers to develop strategic environmental plans and solutions, improving resource allocation efficiency. This collaboration not only enhances resource utilization efficiency and reduces environmental costs but also fosters environmental innovation, providing a competitive advantage in the environmental dimension. (3) Firms need to assess their investments in green supplier practices based on their specific contexts. In scenarios with high levels of government support, firms can strategically plan resource investments in green supplier integration while being cautious and selective when investing in green supplier selection.

5.4. Limitations and Future Research Directions

This study has several significant limitations and opportunities for future research. Firstly, there are notable constraints in terms of geographical scope, methodology, and theoretical framework. The generalizability of the findings is limited as the data collection focused solely on manufacturing firms in China. Caution is advised when applying these results to other international contexts or different manufacturing settings, as varying countries, unique contexts, government policies, and cultural differences may influence management decisions.

Secondly, future research should consider other types of contextual dimensions. For instance, within contexts of regulatory protection or regulatory pressure, it is valuable to investigate which aspects of green supply chain management practices have a more significant positive impact on firms’ environmental performance. This would provide a deeper and more comprehensive understanding and validation of the proposed relationships. Methodologically, there are limitations due to the use of subjective assessment scales to measure firms’ improvements in environmental performance. Integrating objective scales and real-world data would enhance the reliability of the findings and reduce the impact of common method bias. Future research should include data points on regulatory compliance, community impact, and other relevant indicators to provide an objective evaluation of firms’ improvements in environmental performance.

Additionally, expanding the sample size would further enhance the robustness and generalizability of the model. From a theoretical perspective, this study mainly focuses on the moderating role of government support in the relationship between supplier management and environmental performance. However, future research should explore other environmental factor mechanisms, such as market, economic, and technological environments, and their interactions with supplier selection and integration processes. Investigating these interactions and their complex relationships with environmental performance will advance scholars’ understanding of sustainability and green practices, offering a promising direction for future academic inquiry.

6. Conclusions

This study offers a comprehensive analysis of how green supplier selection, green supplier integration, and government support collectively impact the environmental performance of manufacturing firms. Anchored in the Extended Resource-Based View (ERBV) and Contingency Theory (CT), this research explores the influence of contextual factors on green supply chain management. The theoretical framework, along with the implications for theory and practice presented in this study, enhances the existing supplier management literature and provides valuable insights for both scholars and practitioners concerning resource efficiency investments. Given the complexity of sustainability and green supply chain management, it is advisable for manufacturing firms to adapt their supplier management practices to their specific contextual factors. This tailored approach not only fosters higher levels of environmental performance but also strengthens firms’ environmental compliance capabilities, green reputation, and competitive advantage in an increasingly sustainability-driven business environment. These findings offer firms more effective pathways for value creation and strategic directions for sustainable development within resource-constrained operational contexts.